Penalized principal logistic regression for sparse sufficient dimension reduction
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DOI: 10.1016/j.csda.2016.12.003
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References listed on IDEAS
- Lexin Li, 2007. "Sparse sufficient dimension reduction," Biometrika, Biometrika Trust, vol. 94(3), pages 603-613.
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Cited by:
- Pircalabelu, Eugen & Artemiou, Andreas, 2020. "The LassoPSVM approach for sufficient dimension reduction using principal projections," LIDAM Discussion Papers ISBA 2020008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- Jang, Hyun Jung & Shin, Seung Jun & Artemiou, Andreas, 2023. "Principal weighted least square support vector machine: An online dimension-reduction tool for binary classification," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
- Cadirci, Mehmet Siddik & Evans, Dafydd & Leonenko, Nikolai & Makogin, Vitalii, 2022. "Entropy-based test for generalised Gaussian distributions," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
- Pircalabelu, Eugen & Artemiou, Andreas, 2021. "Graph informed sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
- Hayley Randall & Andreas Artemiou & Xingye Qiao, 2021. "Sufficient dimension reduction based on distance‐weighted discrimination," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1186-1211, December.
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Keywords
Max-SCAD penalty; Principal logistic regression; Sparse sufficient dimension reduction; Sufficient dimension reduction;All these keywords.
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